A system for educational assessment without testing is provided that includes one or more client systems that are connected to a network allowing students or school officials to communicate with an education framework that performs and manages educational assessment. The one or more client systems issue a message to the education framework requesting a task to be performed. The educational assessment is administered independent of one or more educators so as to avoid interruption of instruction time. A server system receives the message and the education framework proceeds to process the contents of the message. The education framework includes a plurality of programming modules being executed on the server system that provides to educators specific information used for the educational assessment based on the contents of the message. The programming modules assist in calculating and determining one or more parameters for the educational assessment of the students as well as providing specific reports to educators as to the progress of the students.
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1. An apparatus, comprising: a server system including a processor and a memory, the processor configured to administer an educational assessment at a plurality of client systems via a network connecting the server system to the plurality of client systems, independent of one or more educators so as to avoid interruption of instruction time, the processor configured to monitor use of a language learning system by each student from a plurality of students during the educational assessment, the processor configured to calculate an average usage time of each student from the plurality of students over a period of time based on (1) that student's monitored usage of the language learning system and (2) a plurality of other student performance variables associated with that student, the processor configured to calculate a comparison value for each student from the plurality of students by comparing the average usage time of that student over the period of time to norm sample data, the processor configured to calculate a performance predictor for each student from the plurality of students and in a timely manner based on the comparison value for that student, the performance predictor for a student from the plurality of students indicating that student's chance of meeting a predefined benchmark, the predefined benchmark being based on the norm sample data and indicating a level of instruction intensity for increasing a likelihood that each student from the plurality of students meets the predefined benchmark, the processor configured to, simultaneous to calculating the performance predictor for each student from the plurality of students, generate a skill set identifier associated with each student from the plurality of students, and the processor configured to generate a report including (1) the performance predictor for each student from the plurality of students and (2) a quantified risk variable associated with each student from the plurality of students, the report including a prescription for each student from the plurality of students, the prescription for each student from the plurality of students including (1) a target average usage time for that student, and (2) available lessons to target at that student based at least in part on the skill set identifier associated with that student.
A server-based system assesses student language learning without traditional testing. It connects to student devices via a network and monitors their usage of a language learning system. The system calculates each student's average usage time based on their activity and other performance metrics. This average is compared against norm data to generate a comparison value. Based on this, the system predicts each student's likelihood of meeting a predefined benchmark and suggests an instruction intensity level. Simultaneously, it identifies a skill set level achieved by the student in the language learning system. A report is generated, including the performance predictor, a quantified risk of not meeting the benchmark, a target usage time, and recommended lessons based on the skill set. All this occurs independently of educators to avoid interrupting class time.
2. The apparatus of claim 1 , wherein each student from the plurality of students performs one or more selected daily skill activities during the educational assessment.
The system described previously, where a server-based system assesses student language learning without traditional testing and calculates performance predictors, is enhanced such that each student performs specific daily skill-building exercises within the language learning system as part of the overall educational assessment process. These activities contribute to the data used in determining their performance and progress.
3. The apparatus of claim 1 , wherein the processor is configured to analyze a performance, by a student from the plurality of students, of one or more selected daily skill activities during the educational assessment, so as to calculate the plurality of other student performance variables associated with that student.
In the system where student language learning is assessed and performance predictors are calculated, the server analyzes a student's performance in daily skill-building activities during the assessment. This analysis generates student performance variables, impacting calculations of the average usage time described in the initial system, thereby providing a more comprehensive and nuanced assessment. The analysis is used alongside usage time to calculate average usage time of each student.
4. The apparatus of claim 1 , wherein the predefined benchmark is calculated based on a percentage of students included in the norm sample that completed each level of one or more selected daily activities of the educational assessment.
In the system assessing student language learning and calculating performance predictors, the predefined performance benchmark – the standard a student must meet – is calculated from the norm sample data. Specifically, the benchmark is based on the percentage of students within that norm group who successfully completed each level of the selected daily skill activities incorporated into the educational assessment.
5. The apparatus of claim 1 , wherein the skill set identifier associated with each student from the plurality of students defines an educational level that that student has achieved by using the language learning system.
As part of the automated student language learning assessment, the system generates a skill set identifier for each student, the skill set identifier specifically indicates the student's current educational level or proficiency within the language learning system. This identifier, generated alongside the performance predictor, informs lesson recommendations.
6. The apparatus of claim 1 , wherein: the report is customized for a type of educator; the plurality of students is a plurality of students associated with a first class; and the report further includes performance predictors associated with each student from a plurality of students associated with a second class when the type of educator is a school administrator.
The system assessing student language learning generates customized reports based on the educator's role. If the educator is a teacher of a single class, the report focuses on students in that class. If the educator is a school administrator, the report broadens to include performance predictors for students in multiple classes, providing a wider overview of student progress across the school. The server assesses student language learning and calculates performance predictors.
7. The apparatus of claim 1 , wherein each student from the plurality of students is assigned a usage category based on the average usage time of that student over the period of time.
In the system that assesses student language learning and calculates performance predictors, students are assigned a usage category based on their average usage time of the language learning system over a set period. This categorization could classify students as high, medium, or low usage, influencing the recommended instruction intensity and personalized lessons. The assignment is automated by the server.
8. A method, comprising: administering, to a client system connected to a server system via a network, an educational assessment independent of one or more educators, so as to avoid interruption of instruction time; monitoring use of a language learning system by a student during the educational assessment; calculating, at the server system, an average usage of the language learning system by the student over a period of time based on (1) the monitored use of the language learning system by the student and (2) a plurality of other student performance variables associated with the student; calculating, at the server system, a comparison value for the student by comparing the average usage over the period of time to norm sample data; calculating, at the server system, a performance predictor of the student in a timely manner and based on the comparison value, the performance predictor indicating the student's likelihood of meeting a predefined benchmark, the predefined benchmark being based on the norm sample data, the processor configured to, simultaneous to calculating the performance predictor, generate a skill set identifier associated with the student; generating, at the server system, a report including (1) the performance predictor, (2) a quantified risk variable indicating a risk of failure of the student to meet the predefined benchmark, and (3) a prescription for the student that includes (A) a target average usage for the student, and (B) available lessons to target at the student based at least in part on the skill set identifier associated with the student; and prescribing, at the server system, a level of intensity of instruction for improving the student's likelihood of meeting the predefined benchmark, based on the report.
A method for automated educational assessment without testing involves administering an assessment to students via client devices connected to a server. The system monitors student language learning system usage, then calculates an average usage time, factoring in other performance variables. This average is compared to norm data to calculate a comparison value. The system computes a performance predictor (likelihood of meeting a benchmark) and, simultaneously, generates a skill set identifier. A report details the performance predictor, risk of failure, and provides a prescription: target usage time and lesson recommendations. Based on the report, a level of instruction intensity is prescribed to improve the student's chances of meeting the benchmark.
9. The method of claim 8 , wherein the student performs one or more selected daily skill activities during the educational assessment.
The method described previously, for automated educational assessment and student language learning predictions, is enhanced such that the student performs one or more selected daily skill activities during the educational assessment.
10. The method of claim 8 , further comprising: analyzing the performance, by the student, of one or more selected daily skill activities during the educational assessment, and calculating the plurality of other student performance variables associated with the student based on analysis of the performance of the one or more selected daily skill activities.
The method, for automated educational assessment and student language learning predictions, further includes the step of analyzing the student's performance on one or more selected daily skill activities during the assessment. The analysis is then used to calculate the plurality of other student performance variables mentioned in the base method, which are factored into average usage time.
11. The method of claim 8 , wherein the student performs one or more selected daily skill activities during the educational assessment and the predefined benchmark is calculated based on a percentage of students included in the norm sample data that completed each level of the one or more selected daily skill activities of the educational assessment.
The method for assessing language learning and predicting student performance is enhanced by including daily skill activities. The predefined benchmark used to evaluate student success is determined by calculating the percentage of students in the norm sample data who completed each level of these daily activities. This adds a completion-based metric to the benchmark criteria.
12. The method of claim 8 , wherein the skill set identifier of the student defines an educational level that the student has achieved by using the language learning system.
In the automated language learning assessment method, the skill set identifier assigned to each student reflects their achieved educational level within the language learning system. This identifier helps to tailor personalized recommendations for lessons. The overall method automatically computes a performance predictor.
13. The method of claim 8 , wherein: the report is customized for a type of educator; a first plurality of students is associated with a first class; the report further includes a performance predictor associated with each student from the first plurality of students when the educator type is a teacher; and the report further includes a performance predictor associated with each student from the first plurality of students and a performance predictor associated with each student from a second plurality of students associated with a second class when the educator type is a school administrator.
In the automated educational assessment method, reports are customized by educator type. A teacher views a report for their class. A school administrator views reports for multiple classes. The server assesses student language learning and calculates performance predictors.
14. The method of claim 8 , wherein the student is assigned a usage category based on the average usage of the student over the period of time.
In the method for automated student learning assessment, the student is assigned a usage category based on their average usage of the system over a defined period. The method computes a performance predictor, generates a report, and prescribes lessons.
15. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to: administer, to a client system connected to a server system via a network, an educational assessment independent of one or more educators, so as to avoid interruption of instruction time; monitor, at the server system, use of a language learning system by a student during the educational assessment; calculate an average usage of the language learning system by the student over a period of time based on (1) the monitored use of the language learning system by the student and (2) a plurality of other student performance variables associated with the student; calculate, at the server system, a comparison value for the student by comparing the average usage over the period of time to norm sample data, calculate, at the server system and in a timely manner, a performance predictor of the student based on the comparison value, the performance predictor indicating the student's likelihood of meeting a predefined benchmark, the predefined benchmark being based on the norm sample data, the processor configured to, simultaneous to calculating the performance predictor, generate a skill set identifier associated with the student; generate a report including the performance predictor, a quantified risk variable indicating a risk of failure of the student to meet the predefined benchmark, and a prescription for the student that includes (1) a target average usage time for the student, and (2) available lessons to target at the student based at least in part on the skill set identifier associated with the student; and prescribe a level of intensity of instruction for improving the student's likelihood of meeting the predefined benchmark, based on the report.
A non-transitory computer-readable medium stores instructions for educational assessment without testing. The instructions cause a processor to administer an assessment, monitor language learning system usage, calculate an average usage time considering other performance variables, calculate a comparison value against norm data, and compute a performance predictor. Simultaneously, a skill set identifier is generated. The instructions then generate a report containing the predictor, risk of failure, target usage time, and lesson recommendations. Based on this, the processor prescribes an instruction intensity level to improve the student's chance of meeting a benchmark.
16. The non-transitory processor-readable medium of claim 15 , wherein each student from a plurality of students including the student performs one or more selected daily skill activities during the educational assessment.
The processor-readable medium described previously, which automates student language learning assessment and performance prediction, is enhanced to include instructions to cause each student in a group to complete selected daily skill activities during the assessment.
17. The non-transitory processor-readable medium of claim 15 , wherein the code further comprises code to cause the processor to: analyze a performance, by the student, of one or more selected daily skill activities during the educational assessment; and calculate the plurality of other student performance variables associated with the student based on analysis of the performance of the one or more selected daily skill activities.
The processor-readable medium, which automates student language learning assessment and performance prediction, further includes instructions that cause the processor to analyze a student's performance on selected daily skill activities. It uses this analysis to calculate student performance variables, factored into average usage time calculations.
18. The non-transitory processor-readable medium of claim 15 , wherein the predefined benchmark is calculated based on a percentage of students included in the norm sample that completed each level of one or more selected daily activities of the educational assessment.
The processor-readable medium for assessing student language learning and predicting performance sets the predefined benchmark based on norm sample data. Specifically, the benchmark reflects the percentage of students in the norm sample who completed each level of selected daily skill activities included in the assessment. This utilizes a completion-based metric.
19. The non-transitory processor-readable medium of claim 15 , wherein the skill set identifier of the student defines an educational level that the student has achieved by using the language learning system.
The processor-readable medium for automated language learning assessment generates a skill set identifier, which defines the student's current proficiency level within the language learning system. The skill set is used alongside a performance predictor in a student report.
20. The non-transitory processor-readable medium of claim 15 , wherein the student is assigned a usage category based on the average usage time of the student over the period of time.
Using the processor-readable medium that automates student language learning assessment, the student is assigned a usage category based on their average usage time of the system. This categorization, influencing lesson recommendations, is implemented alongside the performance prediction and report generation.
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March 23, 2016
November 21, 2017
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